Design and Construction of a One‐Dimensional DNA Track for an Artificial Molecular Motor
Bibliographic record
Abstract
DNA is a versatile heteropolymer that shows great potential as a building block for a diverse array of nanostructures. We present here a solution to the problem of designing and synthesizing a DNA‐based nanostructure that will serve as the track along which an artificial molecular motor processes. This one‐dimensional DNA track exhibits periodically repeating elements that provide specific binding sites for the molecular motor. Besides these binding elements, additional sequences are necessary to label specific regions within the DNA track and to facilitate track construction. Designing an ideal DNA track sequence presents a particular challenge because of the many variable elements that greatly expand the number of potential sequences from which the ideal sequence must be chosen. In order to find a suitable DNA sequence, we have adapted a genetic algorithm which is well suited for a large but sparse search space. This algorithm readily identifies long DNA sequences that include all the necessary elements to both facilitate DNA track construction and to present appropriate binding sites for the molecular motor. We have successfully experimentally incorporated the sequence identified by the algorithm into a long DNA track meeting the criteria for observation of the molecular motor′s activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".